Towards dominant resource fairness in virtualized cloud environment

Fair job-scheduling problem in virtualized cloud computing environments has been a focus of research recently, yet this has not received adequate attention in the context of private cloud, where each physical machine hosts a set of virtual machines, while each of virtual machine supports several tasks running on it. In this paper, we introduce a rigorous definition of dominant resource share of tasks and virtual machines, and design a two stage dominant resource fairness scheme that investigate the problem of optimally utilizing a limited amount of a resource (e.g., CPU, memory, bandwidth, or storage) while maximizing application performance and present the inherent tradeoff between resource utilization and fairness in the job-scheduling process. We design a simple heuristic that implements our model in real-world system. Large-scale simulations driven by traces of our cloud platform show that the proposed job scheduling mechanism significantly improves the cloud system's resource utilization, yet with a substantial reduction of job completion times.